@inproceedings{lopez-etal-2019-fine,
title = "Fine-Grained Entity Typing in Hyperbolic Space",
author = "L{\'o}pez, Federico and
Heinzerling, Benjamin and
Strube, Michael",
editor = "Augenstein, Isabelle and
Gella, Spandana and
Ruder, Sebastian and
Kann, Katharina and
Can, Burcu and
Welbl, Johannes and
Conneau, Alexis and
Ren, Xiang and
Rei, Marek",
booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4319",
doi = "10.18653/v1/W19-4319",
pages = "169--180",
abstract = "How can we represent hierarchical information present in large type inventories for entity typing? We study the suitability of hyperbolic embeddings to capture hierarchical relations between mentions in context and their target types in a shared vector space. We evaluate on two datasets and propose two different techniques to extract hierarchical information from the type inventory: from an expert-generated ontology and by automatically mining the dataset. The hyperbolic model shows improvements in some but not all cases over its Euclidean counterpart. Our analysis suggests that the adequacy of this geometry depends on the granularity of the type inventory and the representation of its distribution.",
}
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%0 Conference Proceedings
%T Fine-Grained Entity Typing in Hyperbolic Space
%A López, Federico
%A Heinzerling, Benjamin
%A Strube, Michael
%Y Augenstein, Isabelle
%Y Gella, Spandana
%Y Ruder, Sebastian
%Y Kann, Katharina
%Y Can, Burcu
%Y Welbl, Johannes
%Y Conneau, Alexis
%Y Ren, Xiang
%Y Rei, Marek
%S Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F lopez-etal-2019-fine
%X How can we represent hierarchical information present in large type inventories for entity typing? We study the suitability of hyperbolic embeddings to capture hierarchical relations between mentions in context and their target types in a shared vector space. We evaluate on two datasets and propose two different techniques to extract hierarchical information from the type inventory: from an expert-generated ontology and by automatically mining the dataset. The hyperbolic model shows improvements in some but not all cases over its Euclidean counterpart. Our analysis suggests that the adequacy of this geometry depends on the granularity of the type inventory and the representation of its distribution.
%R 10.18653/v1/W19-4319
%U https://aclanthology.org/W19-4319
%U https://doi.org/10.18653/v1/W19-4319
%P 169-180
Markdown (Informal)
[Fine-Grained Entity Typing in Hyperbolic Space](https://aclanthology.org/W19-4319) (López et al., RepL4NLP 2019)
ACL
- Federico López, Benjamin Heinzerling, and Michael Strube. 2019. Fine-Grained Entity Typing in Hyperbolic Space. In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), pages 169–180, Florence, Italy. Association for Computational Linguistics.